While optical data are often freely available in appropriate quality over large scales, obtaining light detection and ranging (LiDAR) data, which provide valuable information about forest structure, is more cha
To estimate the spatial distribution of planted forests over East Asia, we integrated multi-source planted-natural forest data from multiplein situinventories and digitized data sources in a high-level data fusion algorithm (Fig.1). For each observation, we first created a response variable explicit...
On the basis of satellite data products4,7, our analysis encompasses the heterogeneous spatial and temporal patterns of growth in degraded and secondary forests, influenced by key environmental and anthropogenic drivers. In the first 20 years of recovery, regrowth rates in Borneo were up to 45%...
Data availability The results, calculated as described in theMethods, are based on the data from the Global Forest Watch (https://www.globalforestwatch.org), FAOSTAT (http://www.fao.org/faostat/en/#data/RL), EliScholar (https://elischolar.library.yale.edu/yale_fes_data/1/) and Eora ...
Forest structure was the most important factor explaining the damage rates in old-growth forests (Tables 1 and S3). Tree layer height showed (marginally) significant aggravating effects on the damage rates (except for the category ‘uprooting’, P < 0.076 for all cases; Table S3) while...
including satellite data such as the Global Ecosystem Dynamics Investigation (GEDI)18and Moderate Resolution Imaging Spectroradiometer (MODIS) data to account for potential differences in forest structure and vegetation characteristics between planted and natural forests. In addition to the main products, we...
In annual plants such as agricultural crops, nearly all the C captured in NPP returns to the atmosphere before the next growing season. In trees, a fraction of NPP is integrated into the woody tissues retained in the structure of stems, coarse roots, and branches. Unlike NPP, the Gross ...
3.1 Forest Textures Our strategy for the evaluation of a decision forest on the GPU is to transform the forest's data structure from a list of binary trees to a 2D texture (Figure 4). We lay out the data associated with a tree in a four-component float texture, with each node's ...
The ECE impurity isparametricin the sense that it depends on the hyperparameter\({\mathcal {F}}\)(omitted when clear from context). This dependence is key, as it allows\({\mathsf {m}}_{\mathsf {ece}}\)to incentivize splits that lead to the data being well-fitby\({\mathcal {F...
to quantify the medium-term evolution of root reinforcement and its effect on slope stability in fire-injured forests. In the study, we upscale root reinforcement using field data for the calibration of the Root Bundle Model and detailed information on forest structure in 244 plots, and calculate...